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Associations between fat mass and multi-site pain: a 5-year
longitudinal study
Feng Pan, MD1, Laura Laslett, PhD1, Leigh Blizzard, PhD1, Flavia Cicuttini, MD, PhD 2,
Tania Winzenberg, PhD 1,3, Changhai Ding, MD1, Graeme Jones, MD, PhD 1*
1Menzies Institute for Medical Research, University of Tasmania, Private Bag 23, Hobart,
Tasmania 7000, Australia
2Department of Epidemiology and Preventive Medicine, Monash University Medical School,
Commercial Road, Melbourne 3181, Australia
3Faculty of Health, University of Tasmania, Private Bag 23, Hobart, Tasmania 7000,
Australia
Funding sources: National Health and Medical Research Council of Australia (302204), the
Tasmanian Community Fund (D0015018), the Arthritis Foundation of Australia (MRI06161),
and the University of Tasmania Grant-Institutional Research Scheme (D0015019).
Conflict of interest: None.
Word count: 2,908
Corresponding author:
Graeme Jones*, Menzies Institute for Medical Research, University of Tasmania, Private Bag
23, Hobart, Tasmania 7000, Australia; Tel: +61 3 6226 7705; Fax: +61 3 6226 7704
Email: [email protected]
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Abstract
Objective: Pain is common in older adults and typically involves multiple sites. Obesity is an
important risk factor in the pathogenesis of pain and multi-site pain (MSP). This study aimed
to examine longitudinal associations between fat mass and MSP, and to explore the potential
mechanisms of any associations.
Methods: Data from a longitudinal population-based study of older adults (n=1099) was
utilized with measurements at baseline and after 2.6 and 5.1 years. At each time-point,
presence/absence of pain at the neck, back, hands, shoulders, hips, knees and feet was
assessed by questionnaire. Fat mass was assessed by dual energy x-ray absorptiometry and
height and weight measured.
Results: Participants were of mean age 63 years, mean BMI 27.9 kg/m2 and 51% women.
Participants reporting greater number of painful sites had greater fat mass, fat mass index
(FMI) and BMI both cross-sectionally and longitudinally. In multivariable analyses, fat mass
was associated with MSP (OR, 1.06 per SD; CI 1.02, 1.10) and pain at the hands, knees, hips
and feet (OR=1.29 to 1.99 per SD, all P<0.05). Results were similar for FMI and BMI,
although the latter was also associated with back pain (OR 1.25 per SD; 95% CI 1.02 to
1.54).
Conclusion: Fat mass, FMI and BMI are associated with MSP, pain at all lower limb sites
and hand pain, independent of socio-demographic, physical activity and psychological
factors. This suggests that both loading and systemic inflammatory factors may have an
important role in the pathogenesis of fat-related MSP.
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Keywords: Fat mass, body mass, multi-site pain, mechanism
Significance and Innovations
This is the first longitudinal study to investigate the association between fat mass and
multi-site pain.
Fat mass is associated with pain at multiple sites, pain at all lower limb sites and hand
pain.
These associations are independent of demographic factors, physical activity,
psychological health, education level and employment, suggesting that both loading
and systemic factors may play an important role in the pathogenesis of multi-site pain.
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Introduction
Musculoskeletal pain is common affecting people of all ages (particularly in the elderly with
prevalence estimates of 10%-50%) and often occurs at multiple sites (1-6). A recent study
showed that three quarters of those with musculoskeletal pain have pain at multiple sites (7).
Evidence from previous studies demonstrated that pain at multiple sites is associated with
poorer physical and psychological health, worse health-related quality of life, and disability
when compared to people with pain at a single-site (8-12).
Multi-site pain (MSP), often defined as number of painful sites of two or more, is complex
and multi-factorial, and the underlying mechanisms remain unclear. Risk factors for MSP
include older age (2, 13, 14), female gender (4, 13, 14), physical inactivity (3, 6), lower
educational attainment (7, 14), unemployment (15, 16), psychological distress (3, 8, 13) and
genetic factors (17, 18), although the evidence is inconsistent and may vary by site.
Moreover, body mass index (BMI) or weight can predict the development of pain at different
sites, indicating a possible causal relationship between overweight or obesity and pain (19,
20). It has long been assumed that the mechanism by which overweight or obesity contributes
to pain is due to increased physical loading; however, there is accumulating evidence to
suggest a role of metabolic factors as obesity is linked to a low level of systemic chronic
inflammation. Recent evidence suggests that this may be related in turn to pain (21).
Additionally, loading is insufficient to explaining pain occurring at non-weight bearing sites
such as the hand (6, 22).
BMI is frequently used to measure and classify obesity in the majority of studies
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investigating the association between obesity and pain; however, it cannot adequately
disaggregate the specific components of body composition which have been found to have
different roles in the pathogenesis of musculoskeletal diseases (23). Fat mass is associated
with markers of inflammation in overweight or obese individuals. More recently, studies have
reported a specific detrimental effect of fat mass for low back pain (24) and foot pain (25,
26). Few studies have examined the relationship between fat mass and MSP. Only limited
information is available in two cross-sectional studies (23, 27) which reported a positive
association fat mass with MSP. Such cross-sectional studies cannot determine whether MSP
precedes obesity or vice versa. Also, the studies did not adjust for potential confounders
including socio-demographic, physical activity and psychological factors. Therefore, the aim
of this study was to describe cross-sectional and longitudinal associations between fat mass
and MSP in a population-based sample of older adults, and explore the mechanisms
underlying this relationship.
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Patients and Methods
Participants
This study utilized data from the Tasmanian Older Adult Cohort Study (TASOAC), a
longitudinal, observational population-based study. A total of 1,099 participants aged 50–80
years (mean age 63 years) were randomly selected using computer generated random
numbers from the electoral roll in Southern Tasmania (population 229,000), with an equal
number of men and women. Baseline measures (Phase 1) were conducted in 2002. The
follow-up measures were taken approximately 2.6 years (Phase 2, n=875) and 5.1 years
(Phase 3, n=768) later. The study was approved by the Southern Tasmanian Health and
Medical Human Research Ethics Committee, and all participants provided written informed
consent.
Primary outcome measurement: pain at multi-sites
The location of sites at which the participants experienced pain was self-reported at baseline,
phase 2 and phase 3. Participants were asked whether they had pain (yes/no) in the following
sites: neck, back, hands, shoulders, hips, knees or feet. The total number of painful sites
(range 0 to 7) was categorised into four groups (no pain, 1-2, 3-4 and 5-7 painful sites)
according to the number of painful site groups with approximately equal numbers of
participants reporting one or more painful sites (28).
Primary exposure measurement: body composition
Body composition was measured at baseline, phase 2 and phase 3 by dual-energy X-ray
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absorptiometry (DXA) using a Hologic Delphi densitometer (Model: Hologic Discovery
QDR; Software: Apex system software 2.4.2; Manufacturer: Hologic, Waltham, MA, USA),
which is a quick, non-invasive scan and the gold standard in body composition. A DXA
machine works through producing two very low dose x-ray beams, each with different energy
levels. Differences in densities of each tissue type lead to different levels of absorption which
allow the DXA to calculate their relative masses (29). Fat mass index (FMI) was calculated
as: FMI=fat mass/height2.
Potential covariates measurements
Anthropometrics
Weight was measured to the nearest 0.1 kg (with shoes, socks and bulky clothing removed)
using a single pair of electronic scales (Seca Delta Model 707) calibrated using a known
weight at the beginning of each clinic. Height was measured to the nearest 0.1 cm (with shoes
and socks removed) using a stadiometer. Height and weight were measured at each time-point
and were then used to calculate BMI (kg/m2).
Physical activity
Physical activity was assessed at baseline, phase 2 and phase 3 as steps/day determined by
pedometer (Omron HJ –003 & HJ–102, Omron Healthcare, Kyoto, Japan), as previously
described (30). Briefly, participants were instructed to wear a pedometer for seven
consecutive days and to record the number of steps each day and the duration and type of
physical activity for any activities in which the pedometer could not be worn (for example,
swimming). This was repeated six months later to account for seasonal variation. Mean
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steps/day was calculated as the average of the days worn at both time points.
Emotional problems
Emotional problems were assessed at baseline by asking the question: ‘how much have you
been bothered by emotional problems during the past four weeks, such as feeling anxious,
depressed or irritable?’. Responses included ‘not at all’, ‘very little’, ‘moderately’, ‘quite a
lot’ and ‘extremely’. The presence of emotional problems was defined as a response of ‘very
little’ or more.
Employment
Employment status at baseline was self-reported and collapsed into two categories: employed
(full/part-time) and no paid employment (home duties, student, sole parent/disability pension,
retired or unemployed).
Education level
Participants reported the highest education level they had completed at baseline, which was
collapsed into three categories: low = school only, medium = trade/vocational certificate, high
= university level or above.
Statistical analysis
Mean ± SD and percentages were respectively used to express the continuous and categorical
variables, as noted. ANOVA and ordinal χ2 test (Kruskal-Wallis test) were used to test if there
was a trend of mean of each continuous and categorical variable across pain groups.
Longitudinal data were analysed using mixed-effects models that take repeated observations
on participants into account and use all data on participants. To assess associations of total fat
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mass, FMI and BMI with MSP, mixed-effect models with random intercepts for participants
were used, without and with adjustment for factors, such as age, sex, height, smoking history,
physical activity, emotional problems, education level and employment. Additionally, we
analysed the associations of total fat mass, FMI and BMI with pain at each site pain after
adjusting for the same factors to explore the mechanisms underlying the association. We
tested for interaction between each study factor (total fat mass, FMI and BMI) and follow-up
time, but no significant interactions were found. To compare odds ratios (ORs), fat mass, FMI
and BMI were standardised by dividing by the corresponding standard deviation (SD);
therefore, all ORs represent the odds of pain associated with one SD increase in total fat
mass, FMI or BMI. All statistical analyses were performed using Stata V.12.1 for windows
(StataCorp, College Station, Texas, USA). P values less than 0.05 (two-tailed) were regarded
as statistically significant.
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Results
The participants were on average 63 years old, 51% female and had a mean BMI of 27.9
kg/m2 at baseline. There were 768 participants participating in follow-up over 5.1 years with
three examinations contributing 2,742 person-examinations. Table 1 describes the
characteristics of participants at each examination. Weight, BMI, fat mass and FMI increased
by a small amount over 5.1 years, but physical activity decreased markedly.
The baseline characteristics of participants by number of painful sites are presented in
Table 2. A total of 1,086 participants who had complete data on fat mass and pain were
included into the analyses. 87% of participants had pain in at least one site, with 28% having
pain at one or two sites, 28% having pain at three or four sites, and 31% had pain at five or
more sites. Female sex, higher weight, BMI, fat mass and FMI, lower levels of physical
activity, having emotional problems, being unemployed and having lower education level
were associated with reporting pain at a greater number of painful sites.
Figure 1 shows the association of fat mass and BMI with number of painful sites at each
examination. Fat mass and BMI increased with each category of MSP. However, there was no
statistically significant increase in fat mass or BMI over 5.1 years within any pain category.
Similar results were seen for FMI (data not shown).
The associations of fat mass, FMI and BMI with MSP are shown in Table 3. In
univariable analysis, each SD increase in fat mass, FMI, or BMI was associated with
increased odds of reporting MSP. The associations were reduced but remained statistically
significant after adjusting for age, sex and height, and after further adjusting for smoking
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history, physical activity, emotional problems, education level and employment.
Table 4 presents the associations of fat mass, FMI and BMI with presence of pain at
each site. In univariable and multivariable analysis adjusting for the same confounders as for
MSP, greater fat mass was associated with greater odds of pain in lower limbs (knees, hips
and feet) and hands. Results were similar with FMI and BMI as the outcome, but BMI was
also associated with increased odds of back pain. There were no statistically significant
associations between measures of fat mass and pain at the neck or shoulders in multivariable
analysis.
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Discussion
This study shows that fat mass, FMI and BMI are associated with MSP, pain at weight-
bearing sites and hand pain. These relationships are independent of demographic factors,
physical activity, psychological health, education level and employment, suggesting that fat
mass may play an important independent role in the pathogenesis of MSP. This may reflect a
role of systemic factors in joint pain as one would not expect to observe significant
association of fat mass with hand pain.
The high prevalence of pain at multiple sites is consistent with that reported in previous
studies (3, 7, 13, 23, 31-35), and confirms that MSP is extremely prevalent in a community-
based older population. However, some prior studies have found greater prevalence of MSP
than that found in our study. These discrepancies may be attributed to the difference in the
characteristics of the population studied, definition of MSP and number of painful sites
assessed. Our results also showed that the proportion of people with more than two painful
sites did not change much over time with slightly over half at each visit. This suggests that
MSP is likely to be relatively stable once established (2).
The findings that fat mass, FMI and BMI are associated with increased risk of pain at
multiple sites indicates a substantial effect of fat mass or body weight on the pathogenesis of
MSP. Our findings not only add further evidence to support the significant role of fat mass in
pain, but extend previous cross-sectional studies to longitudinal analyses with a large dataset.
Our results are consistent with previous studies in which BMI was used to examine the
association between overweight/obesity and MSP. In a recent longitudinal study performed in
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the general population, Magnusson et al (20) found that overweight/obesity increased the
odds of reporting MSP. Kamaleri et al (8) found a greater number of painful sites reported in
those with a higher BMI. However, the specific components of body composition cannot be
distinguished in these studies. Currently, there are only two cross-sectional studies
investigating the relationship between body composition and MSP. Brady et al (23) reported
that fat mass is associated with an increased number of lower body pain sites (low back, knee
and foot), and Yoo et al (27) found fat mass to be positively associated with widespread pain.
The potential mechanisms that may link obesity-related pain are most likely physical
loading and metabolic effects (6). Our results showed that fat mass, FMI and BMI are
associated with pain at all weight-bearing sites. This is consistent with previous studies
reporting associations between fat mass and single-site pain (25, 26, 36). These suggest a
potential involvement of biomechanical mechanisms, as excess loading may result in changes
in body mechanics, postures or abnormal gait, thus creating a detrimental biomechanical
environment (37). However, the finding of a significant association of fat mass, FMI and
BMI with hand pain indicates a potential role for metabolic effects in the pathogenesis of
pain, since physical loading is not adequate to explaining pain occurring at non-weight
bearing sites (19, 38). It has been recognized that adipose tissue is serving as an endocrine
organ to produce proinflammatory cytokines and adipokines (39). An increased level of
cytokines and inflammatory markers, such as C-reactive protein (CRP), Interleukins-6 (IL-6),
TNF-alpha (TNF-α) and Leptin observed in obese individuals has been reported in prior
studies (40, 41). Recent evidence suggests that inflammation can lead to a lowering of
excitation threshold and enhanced responses to suprathreshold stimuli of peripheral
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nociceptors (peripheral sensitisation) (42), and subsequently developing central nervous
system sensitisation with pain hypersensitivity and increased vulnerability to reporting more
pain sites (43). It is therefore possible that individuals with greater fat mass are more likely to
have peripheral or central sensitisation in relation to elevated level of systemic inflammation,
thereby leading to a greater number of painful sites.
The current study was unable to detect a significant association between fat mass, FMI
and BMI, and neck and shoulder pain, suggesting that neck and shoulder pain are not related
to fat mass regardless of the mechanism. Consistent with this, Iizuka et al (44) found no
association between fat mass and neck and shoulder pain and concludes that neck and
shoulder pain may be manifest through muscle dysfunction. This is supported by reported
altered muscle activation patterns in patients with neck/shoulder pain with increased
activation of upper trapezius and reduced activation of serratus anterior (45). BMI, but not
other measures of fat mass, was associated with back pain. The reasons for this remain
unclear. Overall, the magnitude of the effect per SD increase for fat mass, FMI and BMI were
similar for all pain sites, suggesting that DXA derived fat mass is not superior to BMI for
accounting for musculoskeletal outcomes.
The current study found that the relationship between fat mass and MSP remained
significant after adjusting for age, sex, height, physical activity, education level, employment,
and psychological distress, suggesting that the associations with fat mass cannot be fully
explained by these factors even if they were themselves associated with pain. Previous
studies have demonstrated that body fat (46) and pain (47) are substantially influenced by
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underlying genetic factors, so it is possible that genetic factors may underlie these
associations. This is supported by a recent meta-analysis (48), in which pooling of the results
from five twin studies on the relationship between obesity and low back pain showed a
positive relationship between BMI or weight and low back pain, but the relationship
diminished after adjusting for shared genetic factors, suggesting that genetic factor may be
mediating the association.
Limitations of our study include the use of a self-reported questionnaire, which was
simple (yes/no) and did not include assessment of frequency, severity, and quality of pain.
We, therefore, cannot evaluate whether fat mass is associated with intensity and different
types of pain. Additionally, assessments were made on only three occasions over 5.1 years –
more frequent observations may allow more information on temporal patterns in fluctuations
in pain. Another limitation is that the participants were recruited from one center, which may
not be generalisable to other populations as previous studies have indicated that special
cultural and socioeconomic conditions may determine individual perceptions affecting the
report of pain (18, 49). Finally, although fat mass is often considered as a surrogate for
systemic inflammation, inflammatory markers were not analysed directly in this study.
Accordingly, further investigation into the role of inflammatory markers in the pathogenesis
of MSP is warranted.
There are several implications raised from the findings of this study. First,
overweight/obese individuals with MSP may benefit from weight loss either via exercise, diet
or bariatric surgery. Therefore, the general practitioner should introduce weight management
programs involving exercise and diet to overweight/obese individuals and encourage them to
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change their lifestyles to lose weight, although there is a considerable challenge in the
maintenance of weight loss in the long-term. Second, the mechanisms by which
overweight/obesity contributes to pain may be not only related to increased physical loading,
but also elevated systemic inflammation; thus, facilitating potential therapeutic targets for
obesity-related pain. For instance, the administration of drugs with pleiotropic actions (anti-
inflammatory and those blocking cholesterol biosynthesis, such as statins) may help to
attenuate pain induction in clinical setting; this would need to be tested in a future clinical
trial.
To sum up, fat mass, FMI and BMI are associated with MSP, pain at all lower limb sites
and hand pain, independent of socio-demographic, physical activity and psychological
factors, suggesting that obesity appears to be an important factor in the pathogenesis of fat-
related MSP. The potential mechanisms underlying the relationship between fat mass and
MSP may be via loading and systemic inflammatory factors.
Author contributions
FP participated in the design of the study, analysis and interpretation of the data and
manuscript preparation. LL participated in the analysis, and revising manuscript. LB
participated in the analysis, interpretation of the data and revising manuscript. FC participated
in the design of the study and revising manuscript. TW participated in interpretation of the
data and revising manuscript. CHD participated in interpretation of the data and revising
manuscript. GJ participated in the design of the study, interpretation of the data and
manuscript preparation. All authors read and approved the final manuscript.
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Acknowledgements
We thank the study participants, who made this study possible, and Catrina Boon and Pip
Boon for their role in collecting the data.
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Figure 1 Association between fat mass/body mass index and the number of painful sites. Bar
graph represents mean value of fat mass/body mass index, and error bars indicate standard
deviations. P for trend determined by ANOVA test. (A) Fat mass; (B) Body mass index.
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Table 1 Characteristics of participants at each examination*
CharacteristicsBaseline(n=1099)
Phase 2(n=875)
Phase 3(n=768)
P value
Age, years 63.0±7.5 65.3±7.3 67.1±7.0 <0.001Female (%) 51 49 50 0.648Height (cm) 167.0±9.0 167.0±9.0 166.6±9.0 0.549Weight (kg) 77.9±15.0 78.1±14.8 78.1±14.8 0.922Body mass index (kg/m2) 27.9±4.8 28.0±4.8 28.1±4.8 0.594Fat mass (kg) 28.3±8.7 28.2±9.0 28.4±8.7 0.954Fat mass index (kg/m2) 10.3±3.6 10.3±3.7 10.4±3.7 0.854Ever smoking (%)† 51 NA NA NAPhysical activity (steps per day)
8614.9±3354.8
7405.2±3358.0
6828.4±3179.8
<0.001
Emotional problems (%)† 64 NA NA NAEmployed (%)† 39 NA NA NAEducation level (%)† NA School only 56 NA NA Vocation training 32 NA NA University or higher 11 NA NAMulti-site joint pain (%) 0.001 No pain 13 19 17 1-2 sites 29 29 31 3-4 sites 28 28 27 5-7 sites 31 24 26
*Values are the Mean±SD except for percentages; †Variables were measured at baseline.
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Table 2 Descriptive characteristics of participants at baseline, by number of painful joints*
Number of painful sitesP
value0(n=137)
1-2(n=310)
3-4(n=303)
5-7(n=336)
Age, years 62.2±7.2 63.6±7.7 62.4±7.2 63.3±7.7 0.676Female (%) 45 48 52 57 0.005Height (cm) 167.4±9.0 167.6±9.4 167.4±8.6 165.9±8.8 0.028Weight (kg) 73.5±12.7 77.3±15.6 79.2±15.3 79.2±14.8 <0.00
1Body mass index (kg/m2)
26.2±3.9 27.4±4.5 28.2±4.5 28.8±5.3 <0.001
Fat mass (kg) 25.0±7.1 27.5±8.5 28.8±8.1 30.0±9.5 <0.001
Fat mass index (kg/m2)
9.1±3.0 9.9±3.4 10.4±3.3 11.1±4.1 <0.001
Ever smoking (%) 46 51 49 55 0.104Physical activity (steps/ day)
9495.1±3579.4
8759.4±3274.9
8560.0±3258.4
8078.2±3341.0
0.001
Emotional problems (%)
53 56 62 70 <0.001
Employed (%) 50 41 42 31 <0.001
Education level (%)
<0.001
School only
49 55 56 61
Vocational training
35 31 30 34
University or higher
16 14 13 5
*Values are the Mean±SD except for percentages; ANOVA and ordinal χ2 test (Kruskal-Wallis test) were used to test if there was a trend of mean of each continuous and categorical variable (increase or decrease) across pain groups.
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Fat mass is associated with multi-site pain
Table 3 Association between fat mass, fat mass index and body mass index and multi-site pain (Number of groups=1086)*
Univariable Multivariable† Multivariable‡
OR 95% CI OR 95% CI OR 95% CI
Fat mass 1.10 1.06, 1.14 1.08 1.04, 1.12 1.06 1.02, 1.10Fat mass index 1.11 1.07, 1.14 1.09 1.05, 1.13 1.07 1.03, 1.11Body mass index 1.09 1.05, 1.12 1.08 1.05, 1.12 1.07 1.04, 1.11
Bold denotes statistically significant result. *OR (95% CI): odds ratio (95% confidence interval) representing the OR of greater number of painful sites associated with per SD increase in fat mass, fat mass index or body mass index;†Fat mass adjusted for age, sex and height; fat mass index and body mass index adjusted for age and sex only.‡Further adjusted for smoking history, physical activity, emotional problems, education level and employment;
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Fat mass is associated with multi-site pain
Table 4 Association between fat mass, fat mass index and body mass index and site-specific pain (Number of groups=1086)*
Pain site Univariable Multivariable† Multivariable‡
OR 95% CI OR 95% CI OR 95% CI
Fat massNeck 1.18 0.97, 1.45 1.04 0.84, 1.29 1.00 0.80, 1.26Back 1.24 1.03, 1.51 1.17 0.95, 1.44 1.20 0.97, 1.49Shoulders 1.27 1.07, 1.51 1.15 0.95, 1.38 1.08 0.89, 1.31Hands 1.54 1.27, 1.86 1.37 1.12, 1.67 1.29 1.04, 1.59Hips 1.53 1.27, 1.84 1.41 1.16, 1.71 1.38 1.13, 1.70Knees 1.96 1.61, 2.39 1.98 1.61, 2.44 1.99 1.59, 2.49Feet 1.89 1.56, 2.28 1.79 1.46, 2.18 1.87 1.51, 2.32
Fat mass indexNeck 1.28 1.05, 1.57 1.05 0.83, 1.33 1.00 0.78, 1.28Back 1.29 1.06, 1.57 1.20 0.95, 1.50 1.22 0.95, 1.56Shoulders 1.39 1.16, 1.65 1.19 0.97, 1.46 1.10 0.88, 1.37Hands 1.73 1.43, 2.11 1.47 1.18, 1.84 1.37 1.08, 1.73Hips 1.62 1.34, 1.95 1.47 1.18, 1.82 1.42 1.13, 1.79Knees 1.86 1.53, 2.27 2.07 1.64, 2.61 2.06 1.60, 2.64Feet 1.95 1.61, 2.36 1.90 1.52, 2.37 1.99 1.57, 2.53
Body mass indexNeck 1.10 0.90, 1.36 1.09 0.89, 1.34 1.06 0.86, 1.31Back 1.23 1.01, 1.50 1.23 1.01, 1.49 1.25 1.02, 1.54Shoulders 1.21 1.02, 1.44 1.20 1.01, 1.42 1.14 0.95, 1.37Hands 1.48 1.22, 1.79 1.46 1.20, 1.77 1.41 1.15, 1.72Hips 1.49 1.23, 1.79 1.46 1.22, 1.76 1.45 1.20, 1.76Knees 1.95 1.60, 2.38 1.94 1.59, 2.37 1.94 1.57, 2.40Feet 1.77 1.46, 2.14 1.75 1.45, 2.12 1.84 1.50, 2.26
Bold denotes statistically significant result. *OR (95% CI): odds ratio (95% confidence interval) representing the OR of greater number of painful sites associated with per SD increase in fat mass, fat mass index or body mass index;†Fat mass adjusted for age, sex and height; fat mass index and body mass index adjusted for age and sex only.‡Further adjusted for smoking history, physical activity, emotional problems, education level and employment;
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